25 changed files with 1398 additions and 610 deletions
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42CLAUDE.md
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44client/dist/chatbot-sdk.js
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2client/dist/chatbot-sdk.js.map
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2client/dist/chatbot-sdk.min.js
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2client/dist/chatbot-sdk.min.js.map
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33client/src/api.ts
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10client/src/chat.ts
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52src/main/java/com/wok/supportbot/config/ChatModelFactory.java
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19src/main/java/com/wok/supportbot/config/DatabaseInitConfig.java
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58src/main/java/com/wok/supportbot/config/DynamicEmbeddingModel.java
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160src/main/java/com/wok/supportbot/config/EmbeddingConfigFixer.java
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190src/main/java/com/wok/supportbot/config/EmbeddingModelFactory.java
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52src/main/java/com/wok/supportbot/config/ModelConfigLoader.java
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40src/main/java/com/wok/supportbot/controller/AiController.java
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5src/main/java/com/wok/supportbot/controller/AiModelConfigController.java
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16src/main/java/com/wok/supportbot/controller/ConversationController.java
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14src/main/java/com/wok/supportbot/rag/load/InMemoryVectorStoreConfig.java
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52src/main/java/com/wok/supportbot/rag/load/PgVectorStoreConfig.java
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13src/main/resources/application.yml
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58src/main/resources/static/components/ModelConfigManager.js
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44src/main/resources/static/sdk/chatbot-sdk.js
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2src/main/resources/static/sdk/chatbot-sdk.js.map
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2src/main/resources/static/sdk/chatbot-sdk.min.js
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2src/main/resources/static/sdk/chatbot-sdk.min.js.map
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1094src/main/resources/static/sdk/test.html
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package com.wok.supportbot.config; |
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import lombok.extern.slf4j.Slf4j; |
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import org.springframework.ai.document.Document; |
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import org.springframework.ai.embedding.EmbeddingModel; |
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import org.springframework.ai.embedding.EmbeddingRequest; |
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import org.springframework.ai.embedding.EmbeddingResponse; |
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import java.util.List; |
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/** |
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* 动态 EmbeddingModel 代理 |
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* 实现 EmbeddingModel 接口，每次调用委托给 EmbeddingModelFactory 当前活跃的实例。 |
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* 这样 PgVectorStore / SimpleVectorStore 无需重建即可在运行时切换向量化模型。 |
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* |
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* 注意：每次调用都走 factory（factory 内部有缓存，开销很小， |
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* 仅在配置变更后第一次调用时才触发新实例创建）。 |
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*/ |
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@Slf4j |
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public class DynamicEmbeddingModel implements EmbeddingModel { |
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private final EmbeddingModelFactory embeddingModelFactory; |
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public DynamicEmbeddingModel(EmbeddingModelFactory embeddingModelFactory) { |
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this.embeddingModelFactory = embeddingModelFactory; |
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} |
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private EmbeddingModel getDelegate() { |
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return embeddingModelFactory.getEmbeddingModel(); |
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} |
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@Override |
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public float[] embed(Document document) { |
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return getDelegate().embed(document); |
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} |
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@Override |
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public EmbeddingResponse embedForResponse(List<String> texts) { |
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return getDelegate().embedForResponse(texts); |
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} |
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@Override |
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public EmbeddingResponse call(EmbeddingRequest request) { |
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return getDelegate().call(request); |
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} |
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@Override |
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public int dimensions() { |
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return getDelegate().dimensions(); |
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} |
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/** |
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* 返回当前委托的 EmbeddingModel 实例，供调试/日志使用 |
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*/ |
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public EmbeddingModel getCurrentDelegate() { |
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return getDelegate(); |
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} |
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} |
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package com.wok.supportbot.config; |
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import lombok.extern.slf4j.Slf4j; |
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import org.springframework.ai.embedding.EmbeddingModel; |
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import org.springframework.beans.factory.annotation.Autowired; |
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import org.springframework.beans.factory.annotation.Value; |
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import org.springframework.boot.context.event.ApplicationReadyEvent; |
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import org.springframework.context.ApplicationListener; |
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import org.springframework.jdbc.core.JdbcTemplate; |
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import org.springframework.stereotype.Component; |
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import java.util.List; |
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import java.util.Map; |
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/** |
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* Embedding 配置检查器 |
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* 在应用启动完成后，检查 EMBEDDING 配置的合理性并记录日志。 |
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* 注意：不再强制修正非 DashScope 配置，尊重用户在 DB 中配置的提供商和模型。 |
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* 新增：检测 EmbeddingModel 返回的实际维度与 yml 配置的向量维度是否一致。 |
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*/ |
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@Component |
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@Slf4j |
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public class EmbeddingConfigFixer implements ApplicationListener<ApplicationReadyEvent> { |
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@Autowired |
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private JdbcTemplate jdbcTemplate; |
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@Autowired |
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private EmbeddingModelFactory embeddingModelFactory; |
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@Value("${spring.ai.dashscope.api-key:}") |
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private String dashscopeApiKey; |
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@Value("${knowledge.vector.dimension:1536}") |
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private int fallbackDimension; |
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@Override |
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public void onApplicationEvent(ApplicationReadyEvent event) { |
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try { |
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checkEmbeddingConfig(); |
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checkDimensionConsistency(); |
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} catch (Exception e) { |
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log.warn("Embedding 配置检查异常（不影响启动）: {}", e.getMessage()); |
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} |
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} |
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private void checkEmbeddingConfig() { |
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// 打印当前 EMBEDDING 所有配置 |
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List<Map<String, Object>> allConfigs = jdbcTemplate.queryForList( |
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"SELECT id, name, provider, model_name, api_key, base_url, is_active, is_delete " + |
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"FROM ai_model_config WHERE app_type = 'EMBEDDING' ORDER BY is_active DESC"); |
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log.info("========== EMBEDDING 配置检查 =========="); |
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for (Map<String, Object> row : allConfigs) { |
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String apiKey = (String) row.get("api_key"); |
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String maskedKey = apiKey != null && apiKey.length() > 8 |
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? apiKey.substring(0, 4) + "****" + apiKey.substring(apiKey.length() - 4) |
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: "****"; |
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log.info(" id={} name={} provider={} model={} apiKey={} baseUrl={} active={} deleted={}", |
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row.get("id"), row.get("name"), row.get("provider"), |
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row.get("model_name"), maskedKey, row.get("base_url"), |
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row.get("is_active"), row.get("is_delete")); |
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} |
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// 查找活跃配置 |
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Map<String, Object> activeConfig = null; |
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for (Map<String, Object> row : allConfigs) { |
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if (Boolean.TRUE.equals(row.get("is_active")) |
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&& Boolean.FALSE.equals(row.get("is_delete"))) { |
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activeConfig = row; |
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break; |
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} |
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} |
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if (activeConfig == null) { |
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log.warn("EMBEDDING 类型无活跃配置，请在前端管理页面配置"); |
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return; |
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} |
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String apiKey = (String) activeConfig.get("api_key"); |
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String modelName = (String) activeConfig.get("model_name"); |
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String provider = (String) activeConfig.get("provider"); |
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String baseUrl = (String) activeConfig.get("base_url"); |
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// 仅记录警告，不再强制改写（用户可能有意使用非 DashScope 的 Embedding 提供商） |
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if (!"dashscope".equalsIgnoreCase(provider)) { |
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log.info("EMBEDDING 使用非 DashScope 提供商 [{}]，model={}。" |
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+ "请确保前端「向量维度」与实际模型输出一致，否则需重建 vector_store 表", provider, modelName); |
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} |
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// DashScope 用户的 API Key 一致性提示（不强制修正，尊重用户 DB 配置） |
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if ("dashscope".equalsIgnoreCase(provider) |
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&& dashscopeApiKey != null && !dashscopeApiKey.isBlank() |
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&& !dashscopeApiKey.equals(apiKey)) { |
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log.warn("EMBEDDING DashScope 配置的 API Key 与 application.yml 不一致，以 DB 配置为准。" |
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+ "如需统一管理，请在管理页面修改。"); |
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} |
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if (baseUrl != null && !baseUrl.isBlank()) { |
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log.debug("EMBEDDING 配置了自定义 baseUrl={}", baseUrl); |
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} |
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log.info("EMBEDDING 配置检查完成，当前使用 provider={} model={}", provider, modelName); |
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} |
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/** |
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* 检测 EmbeddingModel 实际返回的维度与配置的向量维度是否一致。 |
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* 配置维度优先级：DB extraConfig.dimensions > application.yml knowledge.vector.dimension。 |
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*/ |
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private void checkDimensionConsistency() { |
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try { |
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int configuredDimension = resolveConfiguredDimension(); |
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EmbeddingModel model = embeddingModelFactory.getEmbeddingModel(); |
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int actualDimension = model.dimensions(); |
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if (actualDimension > 0 && actualDimension != configuredDimension) { |
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log.warn("========== ⚠️ 向量维度不匹配！=========="); |
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log.warn("Embedding 模型 {} 返回维度: {}", model, actualDimension); |
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log.warn("当前配置的向量维度: {}", configuredDimension); |
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log.warn("请执行以下步骤修复："); |
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log.warn(" 1. 在前端「AI 大模型配置管理」中修改 EMBEDDING 配置的向量维度为 {}", actualDimension); |
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log.warn(" 2. 在 PostgreSQL 中执行: DROP TABLE IF EXISTS vector_store CASCADE"); |
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log.warn(" 3. 重启服务（PgVectorStore 会自动重建表）"); |
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log.warn(" 4. 重新上传所有知识库文档"); |
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log.warn("========================================"); |
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} else { |
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log.info("向量维度校验通过: Embedding 模型返回 {} 维，配置 {} 维，一致", |
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actualDimension, configuredDimension); |
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} |
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} catch (Exception e) { |
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log.warn("无法校验向量维度（EmbeddingModel 初始化可能失败）: {}", e.getMessage()); |
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} |
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} |
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/** |
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* 解析配置维度：DB extraConfig 优先，yml 兜底。 |
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*/ |
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private int resolveConfiguredDimension() { |
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try { |
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List<Map<String, Object>> activeConfigs = jdbcTemplate.queryForList( |
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"SELECT extra_config FROM ai_model_config WHERE app_type = 'EMBEDDING' AND is_active = true AND is_delete = false LIMIT 1"); |
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if (!activeConfigs.isEmpty()) { |
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Map<String, Object> row = activeConfigs.get(0); |
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String extraConfigJson = (String) row.get("extra_config"); |
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// 简单解析 JSONB 中的 dimensions 字段 |
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if (extraConfigJson != null && extraConfigJson.contains("\"dimensions\"")) { |
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java.util.regex.Pattern p = java.util.regex.Pattern.compile("\"dimensions\"\\s*:\\s*(\\d+)"); |
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java.util.regex.Matcher m = p.matcher(extraConfigJson); |
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if (m.find()) { |
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int dim = Integer.parseInt(m.group(1)); |
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log.info("从 DB EMBEDDING extraConfig 读取维度: {}", dim); |
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return dim; |
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} |
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} |
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} |
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} catch (Exception e) { |
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log.warn("从 DB 读取 EMBEDDING 维度失败: {}", e.getMessage()); |
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} |
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log.info("使用 application.yml fallback 维度: {}", fallbackDimension); |
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return fallbackDimension; |
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} |
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} |
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@ -0,0 +1,190 @@ |
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package com.wok.supportbot.config; |
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import com.alibaba.cloud.ai.dashscope.api.DashScopeApi; |
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import com.alibaba.cloud.ai.dashscope.embedding.DashScopeEmbeddingModel; |
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import com.alibaba.cloud.ai.dashscope.embedding.DashScopeEmbeddingOptions; |
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import com.wok.supportbot.entity.AiModelConfig; |
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import com.wok.supportbot.service.AiModelConfigService; |
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import lombok.extern.slf4j.Slf4j; |
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import org.springframework.ai.document.MetadataMode; |
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import org.springframework.ai.embedding.EmbeddingModel; |
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import org.springframework.ai.openai.OpenAiEmbeddingModel; |
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import org.springframework.ai.openai.OpenAiEmbeddingOptions; |
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import org.springframework.ai.openai.api.OpenAiApi; |
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import org.springframework.beans.factory.annotation.Autowired; |
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import org.springframework.retry.backoff.ExponentialBackOffPolicy; |
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import org.springframework.retry.support.RetryTemplate; |
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import org.springframework.stereotype.Component; |
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import java.util.Map; |
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import java.util.concurrent.ConcurrentHashMap; |
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/** |
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* EmbeddingModel 工厂 |
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* 按 DB 活跃配置（EMBEDDING 类型）动态创建/缓存 EmbeddingModel 实例，支持运行时切换提供商。 |
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* - DashScope：手动构造 DashScopeApi + DashScopeEmbeddingModel |
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* - DeepSeek / Kimi / 豆包 / 智谱 / OpenAI 等：通过 spring-ai-openai 模块 + 自定义 baseUrl 创建 |
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*/ |
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@Component |
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@Slf4j |
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public class EmbeddingModelFactory { |
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@Autowired |
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private AiModelConfigService configService; |
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/** |
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* EmbeddingModel 缓存：key = "provider:apiKey:modelName" |
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*/ |
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private final ConcurrentHashMap<String, EmbeddingModel> cache = new ConcurrentHashMap<>(); |
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/** |
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* 各提供商默认的 API 基础地址 |
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*/ |
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private static final Map<String, String> DEFAULT_BASE_URLS = Map.of( |
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"deepseek", "https://api.deepseek.com", |
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"moonshot", "https://api.moonshot.cn/v1", |
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"volcengine", "https://ark.cn-beijing.volces.com/api/v3", |
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"zhipu", "https://open.bigmodel.cn/api/paas/v4", |
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"openai", "https://api.openai.com" |
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); |
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/** |
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* 各提供商的 Embedding API 路径（相对于 baseUrl） |
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* OpenAI 默认是 /v1/embeddings，但许多国产厂商在 baseUrl 中已包含版本号 |
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* （如 /v1、/api/v3、/api/paas/v4），因此只需 /embeddings 即可。 |
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* 豆包多模态模型需使用 /embeddings/multimodal（与 OpenAI 请求格式不兼容，另行处理）。 |
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*/ |
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private static final Map<String, String> EMBEDDINGS_PATHS = Map.of( |
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"deepseek", "/v1/embeddings", |
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"moonshot", "/embeddings", // baseUrl 已含 /v1 |
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"volcengine", "/embeddings", // baseUrl 已含 /api/v3 |
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"zhipu", "/embeddings", // baseUrl 已含 /api/paas/v4 |
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"openai", "/v1/embeddings" |
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); |
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/** |
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* 获取当前活跃的 EmbeddingModel |
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* 查询 DB 中 app_type=EMBEDDING 的活跃配置，无配置时使用 DashScope text-embedding-v2 作为兜底 |
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* |
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* @return EmbeddingModel 实例 |
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*/ |
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public EmbeddingModel getEmbeddingModel() { |
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AiModelConfig config = configService.getActiveConfigWithFullKey("EMBEDDING"); |
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if (config == null || config.getApiKey() == null || config.getApiKey().isBlank()) { |
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log.warn("EMBEDDING 类型无活跃配置，请在管理页面配置向量化模型"); |
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throw new IllegalStateException("EMBEDDING 类型无活跃配置，请在管理页面配置向量化模型"); |
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} |
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log.debug("获取 EMBEDDING 活跃配置: provider={}, modelName={}, apiKey前4位={}", |
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config.getProvider(), config.getModelName(), |
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config.getApiKey().substring(0, Math.min(4, config.getApiKey().length()))); |
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return getOrCreateEmbeddingModel(config); |
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} |
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/** |
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* 获取或创建 EmbeddingModel（带缓存） |
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* 缓存 key = provider:apiKey:modelName，配置不变则复用实例 |
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*/ |
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private EmbeddingModel getOrCreateEmbeddingModel(AiModelConfig config) { |
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String cacheKey = config.getProvider() + ":" + config.getApiKey() + ":" + config.getModelName(); |
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return cache.computeIfAbsent(cacheKey, k -> createEmbeddingModel(config)); |
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} |
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/** |
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* 创建 EmbeddingModel 实例 |
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* - dashscope：手动构造 DashScopeApi + DashScopeEmbeddingModel |
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* - volcengine 豆包多模态模型：抛出异常提示选用纯文本模型 |
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* - 其他提供商：通过 OpenAI 兼容 API 创建 |
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*/ |
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private EmbeddingModel createEmbeddingModel(AiModelConfig config) { |
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RetryTemplate retryTemplate = createRetryTemplate(); |
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if ("dashscope".equalsIgnoreCase(config.getProvider())) { |
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log.info("创建 DashScope EmbeddingModel: model={}", config.getModelName()); |
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DashScopeApi api = DashScopeApi.builder() |
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.apiKey(config.getApiKey()) |
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.build(); |
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DashScopeEmbeddingOptions options = DashScopeEmbeddingOptions.builder() |
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.withModel(config.getModelName()) |
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.build(); |
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return new DashScopeEmbeddingModel(api, MetadataMode.EMBED, options, retryTemplate); |
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} |
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// 豆包多模态模型检测：doubao-embedding-vision* 使用 /embeddings/multimodal 端点， |
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// 请求/响应格式与 OpenAI 不兼容，无法通过 OpenAiEmbeddingModel 调用。 |
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|
// 提示用户改用纯文本模型。 |
||||
|
if ("volcengine".equalsIgnoreCase(config.getProvider()) |
||||
|
&& config.getModelName() != null |
||||
|
&& config.getModelName().contains("vision")) { |
||||
|
throw new IllegalArgumentException( |
||||
|
"豆包多模态向量模型 (" + config.getModelName() + ") 不支持当前使用的 OpenAI 兼容 API 格式。" |
||||
|
+ "请将 EMBEDDING 模型名称改为纯文本模型，如 doubao-embedding-text-240515（2048维）或 doubao-embedding-large（4096维）。" |
||||
|
+ "注意：切换向量维度后需调整 PgVectorStoreConfig.dimensions 并重建 vector_store 表。"); |
||||
|
} |
||||
|
|
||||
|
// OpenAI 兼容提供商 |
||||
|
String baseUrl = resolveBaseUrl(config); |
||||
|
String embeddingsPath = resolveEmbeddingsPath(config); |
||||
|
log.info("创建 OpenAI 兼容 EmbeddingModel: provider={}, baseUrl={}, embeddingsPath={}, model={}", |
||||
|
config.getProvider(), baseUrl, embeddingsPath, config.getModelName()); |
||||
|
|
||||
|
// 维度一致性提示 |
||||
|
log.info("⚠ 请确认模型 [{}] 的向量维度与 PgVectorStoreConfig.dimensions(1536) 一致，否则需调整并重建向量表", |
||||
|
config.getModelName()); |
||||
|
|
||||
|
OpenAiApi api = OpenAiApi.builder() |
||||
|
.apiKey(config.getApiKey()) |
||||
|
.baseUrl(baseUrl) |
||||
|
.embeddingsPath(embeddingsPath) |
||||
|
.build(); |
||||
|
|
||||
|
OpenAiEmbeddingOptions options = OpenAiEmbeddingOptions.builder() |
||||
|
.model(config.getModelName()) |
||||
|
.build(); |
||||
|
|
||||
|
return new OpenAiEmbeddingModel(api, MetadataMode.EMBED, options, retryTemplate); |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 解析 API 基础地址：优先使用 DB 配置的 baseUrl，否则使用提供商默认值 |
||||
|
*/ |
||||
|
private String resolveBaseUrl(AiModelConfig config) { |
||||
|
if (config.getBaseUrl() != null && !config.getBaseUrl().isBlank()) { |
||||
|
return config.getBaseUrl(); |
||||
|
} |
||||
|
String defaultUrl = DEFAULT_BASE_URLS.get(config.getProvider()); |
||||
|
if (defaultUrl != null) { |
||||
|
return defaultUrl; |
||||
|
} |
||||
|
throw new IllegalArgumentException( |
||||
|
"未知提供商 [" + config.getProvider() + "]，请在配置中填写 API 基础地址 (baseUrl)"); |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 解析 Embedding API 路径：优先使用提供商已知路径，否则使用 OpenAI 默认 /v1/embeddings |
||||
|
*/ |
||||
|
private String resolveEmbeddingsPath(AiModelConfig config) { |
||||
|
String known = EMBEDDINGS_PATHS.get(config.getProvider()); |
||||
|
return known != null ? known : "/v1/embeddings"; |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 创建简单的重试模板（最多 3 次，指数退避） |
||||
|
*/ |
||||
|
private RetryTemplate createRetryTemplate() { |
||||
|
RetryTemplate retryTemplate = new RetryTemplate(); |
||||
|
ExponentialBackOffPolicy backOffPolicy = new ExponentialBackOffPolicy(); |
||||
|
backOffPolicy.setInitialInterval(1000); |
||||
|
backOffPolicy.setMultiplier(2); |
||||
|
backOffPolicy.setMaxInterval(10000); |
||||
|
retryTemplate.setBackOffPolicy(backOffPolicy); |
||||
|
return retryTemplate; |
||||
|
} |
||||
|
|
||||
|
/** |
||||
|
* 清除 EmbeddingModel 缓存（配置变更时调用） |
||||
|
*/ |
||||
|
public void clearCache() { |
||||
|
cache.clear(); |
||||
|
log.info("EmbeddingModel 缓存已清除"); |
||||
|
} |
||||
|
} |
||||
@ -1,20 +1,26 @@ |
|||||
package com.wok.supportbot.rag.load; |
package com.wok.supportbot.rag.load; |
||||
|
|
||||
import org.springframework.ai.embedding.EmbeddingModel; |
|
||||
|
import com.wok.supportbot.config.DynamicEmbeddingModel; |
||||
|
import com.wok.supportbot.config.EmbeddingModelFactory; |
||||
import org.springframework.ai.vectorstore.SimpleVectorStore; |
import org.springframework.ai.vectorstore.SimpleVectorStore; |
||||
import org.springframework.ai.vectorstore.VectorStore; |
import org.springframework.ai.vectorstore.VectorStore; |
||||
|
import org.springframework.beans.factory.annotation.Autowired; |
||||
import org.springframework.context.annotation.Bean; |
import org.springframework.context.annotation.Bean; |
||||
import org.springframework.context.annotation.Configuration; |
import org.springframework.context.annotation.Configuration; |
||||
|
|
||||
/** |
/** |
||||
* 向量数据库配置（初始化基于内存的向量数据库 Bean） |
* 向量数据库配置（初始化基于内存的向量数据库 Bean） |
||||
|
* 使用 DynamicEmbeddingModel 代理，支持运行时切换向量化模型无需重启 |
||||
*/ |
*/ |
||||
@Configuration |
@Configuration |
||||
public class InMemoryVectorStoreConfig { |
public class InMemoryVectorStoreConfig { |
||||
|
|
||||
|
@Autowired |
||||
|
private EmbeddingModelFactory embeddingModelFactory; |
||||
|
|
||||
@Bean |
@Bean |
||||
VectorStore inMemoryVectorStore(EmbeddingModel dashscopeEmbeddingModel) { |
|
||||
SimpleVectorStore simpleVectorStore = SimpleVectorStore.builder(dashscopeEmbeddingModel).build();; |
|
||||
return simpleVectorStore; |
|
||||
|
VectorStore inMemoryVectorStore() { |
||||
|
DynamicEmbeddingModel dynamicEmbeddingModel = new DynamicEmbeddingModel(embeddingModelFactory); |
||||
|
return SimpleVectorStore.builder(dynamicEmbeddingModel).build(); |
||||
} |
} |
||||
} |
} |
||||
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